Seaweed biomass as a sustainable resource for synthesis of ZnO nanoparticles using Sargassum wightii ethanol extract and their environmental and biomedical applications through Gaussian mixture model

Yu Bai; Yan Cao; Yiding Sun; Faiz Abdulaziz Alfaiz; Hakim A.L. Garalleh; E.F. El-Shamy; Hamad Almujibah; Elimam Ali; Hamid Assilzadeh

doi:10.1016/j.envres.2023.117464

Seaweed biomass as a sustainable resource for synthesis of ZnO nanoparticles using Sargassum wightii ethanol extract and their environmental and biomedical applications through Gaussian mixture model

Journal

Environmental Research

ISSN

0013-9351

Date Issued

2024-05

Author(s)

Yu Bai

Yan Cao

Yiding Sun

Faiz Abdulaziz Alfaiz

Hakim A.L. Garalleh

E.F. El-Shamy

Hamad Almujibah

Elimam Ali

Hamid Assilzadeh

Facultad de Arquitectura y Urbanismo

DOI

10.1016/j.envres.2023.117464

URL

https://cris.ute.edu.ec/handle/123456789/136

Abstract

Zinc oxide nanoparticles (ZnO) possess unique features that mak them a common matter among different industries. Nevertheless, traditional models of synthesizing ZnO-NPs are related with health and environmental and risks due to harmful chemicals. The biosynthesis of zinc oxide nanoparticles was achieved using the hot water extract of Sargassum wightii (SW), which serves as a reducing agent. This extract is mixed with zinc precursors, initiating a bio-reduction process. UV–vis, FTIR, XRD, Raman, DLS, SEM, EDX, TEM imaging, and XPS analysis are used. The novelty of this research lies in utilizing a bio-reduction process involving hot water extract of SW to synthesize zinc oxide nanoparticles, providing a safer and eco-friendly alternative to traditional chemical methods. Here, the zinc oxide nanoparticles produced through the biosynthesis process effectively addressed oral infections (Streptococcus mutans) due to their ability to disrupt the integrity of bacterial cell membranes, interfere with cellular processes, and inhibit the growth and proliferation of bacteria responsible for oral infections. Gaussian Mixture Models (GMMs) uncover intricate patterns within medical data, enabling enhanced diagnostics, treatment personalization, and patient outcomes. This study aims to apply Gaussian Mixture Models (GMMs) to medical data for subpopulation identification and disease subtyping, contributing to personalized treatment strategies and improved patient care. With a dataset comprising 300 samples, the application of GMM showed lower BIC and AIC values (2500, 3200), a high Silhouette Score (0.65 from −1 to 1) reflecting well-defined clusters, Calinski-Harabasz (120) and Davies-Bouldin Indices (0.45). These metrics collectively underscored the model's success in revealing distinct patterns within the data. ZnO-nanocoated aligners were effective against Streptococcus mutans, with the maximum antibacterial effect observed for 2 days and lasting for 7 days.